The Elements of Statistical Learning

Support vector machines for predicting distribution of Sudden Oak Death in California

Guo, Q., Kelly, M., & Graham, C. H. (2004) · Ecological Modelling · doi:10.1016/j.ecolmodel.2004.07.012 · Cited by 347

Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes

Austin, P. C., Tu, J. V., Ho, J. E., Levy, D. A., & Lee, D. S. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.11.008 · Cited by 350

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

Olson, R. S., Urbanowicz, R. J., Andrews, P. C., Lavender, N., Kidd, L. R., & Moore, J. H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31204-0_9 · Cited by 395

A bagging SVM to learn from positive and unlabeled examples

Mordelet, F., & Vert, J. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.06.010 · Cited by 358

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Wisz, M. S., & Guisan, A. (2009) · BMC Ecology · doi:10.1186/1472-6785-9-8 · Cited by 364

Support vector machines and its applications in chemistry

Li, H., Liang, Y., & Xu, Q. (2008) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2008.10.007 · Cited by 342

Genomic Variation in Natural Populations ofDrosophila melanogaster

Langley, C. H., Stevens, K., Cardeno, C., Lee, Y. C. G., Schrider, D. R., Pool, J. E., Langley, S. A., Suarez, C., Corbett-Detig, R. B., Kolaczkowski, B., et al. (2012) · Genetics · doi:10.1534/genetics.112.142018 · Cited by 340

Adaptive regularization of weight vectors

Crammer, K., Kulesza, A., & Dredze, M. H. (2013) · Machine Learning · doi:10.1007/s10994-013-5327-x · Cited by 340

Above-ground carbon storage by urban trees in Leipzig, Germany: Analysis of patterns in a European city

Strohbach, M. W., & Haase, D. (2011) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2011.10.001 · Cited by 359

Propensity Scores

Luellen, J. K., Shadish, W. R., & Clark, M. H. (2005) · Evaluation Review · doi:10.1177/0193841x05275596 · Cited by 354

Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling

Pourghasemi, H. R., Yousefi, S., Kornejady, A., & Cerdà, A. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.07.198 · Cited by 337

Stock market prediction using machine learning classifiers and social media, news

Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01839-w · Cited by 336

Bayesian networks in biomedicine and health-care

Lucas, P., van der Gaag, L. C., & Abu–Hanna, A. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2003.11.001 · Cited by 344

Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

Plitt, M., Barnes, K. A., & Martin, A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.12.013 · Cited by 335

Pattern-based local linear regression models for short-term load forecasting

Dudek, G. (2015) · Electric Power Systems Research · doi:10.1016/j.epsr.2015.09.001 · Cited by 342

Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)

Guo, D. (2008) · International Journal of Geographical Information Systems · doi:10.1080/13658810701674970 · Cited by 336

SVM-RFE With MRMR Filter for Gene Selection

Mundra, P. A., & Rajapakse, J. C. (2009) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2009.2035284 · Cited by 334

Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5960 · Cited by 333

A comparative analysis of machine learning techniques for student retention management

Delen, D. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.06.003 · Cited by 377

Fast exact leave-one-out cross-validation of sparse least-squares support vector machines

Cawley, G. C., & Talbot, N. L. C. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.07.002 · Cited by 337

Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish

Leathwick, J. R., Rowe, D. K., RICHARDSON, J., Elith, J., & Hastie, T. J. (2005) · Freshwater Biology · doi:10.1111/j.1365-2427.2005.01448.x · Cited by 337

Learning query intent from regularized click graphs

Li, X., Wang, Y., & Acero, A. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390393 · Cited by 332

MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction

Varadarajan, B., Hefny, A., Srivastava, A., Refaat, K. S., Nayakanti, N., Cornman, A., Chen, K., Douillard, B., Lam, C. P., Anguelov, D., et al. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812107 · Cited by 382

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

Lughofer, E. D. (2008) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2008.925908 · Cited by 331

Identifying causal gateways and mediators in complex spatio-temporal systems

Runge, J., Petoukhov, V. K., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., Hartman, D. G., Marwan, N., Paluš, M., & Kurths, J. (2015) · Nature Communications · doi:10.1038/ncomms9502 · Cited by 337

Multivariate comparison of classification performance measures

Ballabio, D., Grisoni, F., & Todeschini, R. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.12.004 · Cited by 342

Compressive spatial summation in human visual cortex

Kay, K., Winawer, J. A., Mezer, A., & Wandell, B. A. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00105.2013 · Cited by 347

Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation

Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2015.07.002 · Cited by 328

Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Škrjanc, I., Iglesias, J. A., Sanchis, A., Leite, D. F., Lughofer, E. D., & Gomide, F. A. C. (2019) · Information Sciences · doi:10.1016/j.ins.2019.03.060 · Cited by 329

The fourth‐corner solution – using predictive models to understand how species traits interact with the environment

Brown, A. M., Warton, D. I., Andrew, N. R., Binns, M. R., Cassis, G., & Gibb, H. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12163 · Cited by 329

Distinguishing cell types or populations based on the computational analysis of their infrared spectra

Martin, F. L., Kelly, J. G., Llabjani, V., Martin‐Hirsch, P. L., Patel, I. I., Trevisan, J., Fullwood, N. J., & Walsh, M. J. (2010) · Nature Protocols · doi:10.1038/nprot.2010.133 · Cited by 336

The k-means clustering technique: General considerations and implementation in Mathematica

Morissette, L., & Chartier, S. (2013) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.09.1.p015 · Cited by 355

Machine learning, statistical learning and the future of biological research in psychiatry

Iniesta, R., Stahl, D., & McGuffin, P. (2016) · Psychological Medicine · doi:10.1017/s0033291716001367 · Cited by 328

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

Wager, T. D., Atlas, L. Y., Leotti, L. A., & Rilling, J. K. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3420-10.2011 · Cited by 328

State-dependent architecture of thalamic reticular subnetworks

Halassa, M. M., Chen, Z., Wimmer, R. D., Brunetti, P. M., Zhao, S., Zikopoulos, B., Wang, F., Brown, E. N., & Wilson, M. A. (2014) · Cell · doi:10.1016/j.cell.2014.06.025 · Cited by 309

The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering

Li, T., & Ding, C. H. Q. (2006) · Proceedings · doi:10.1109/icdm.2006.160 · Cited by 327

Machine learning algorithms for construction projects delay risk prediction

Gondia, A., Siam, A., El-Dakhakhni, W., & Nassar, A. H. (2020) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001736 · Cited by 327

Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models

Storlie, C. B., Swiler, L. P., Helton, J. C., & Sallaberry, C. J.-M. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2009.05.007 · Cited by 329

Efficiently exploring architectural design spaces via predictive modeling

İpek, E., McKee, S. A., Caruana, R., de Supinski, B. R., & Schulz, M. (2006) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/1168857.1168882 · Cited by 329

Electronic medical records for discovery research in rheumatoid arthritis

Liao, K. P., Cai, T., Gainer, V. S., Goryachev, S. D., Zeng‐Treitler, Q., Raychaudhuri, S., Szolovits, P., Churchill, S. E., Murphy, S. N., Kohane, I. S., et al. (2010) · Arthritis Care & Research · doi:10.1002/acr.20184 · Cited by 320

Word spotting for historical documents

Rath, T. M., & Manmatha, R. (2006) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-006-0035-8 · Cited by 325

Deep neural networks enable quantitative movement analysis using single-camera videos

Kidziński, Ł., Yang, B., Hicks, J. L., Rajagopal, A., Delp, S. L., & Schwartz, M. H. (2020) · Nature Communications · doi:10.1038/s41467-020-17807-z · Cited by 343

Temporal Filtering of Reward Signals in the Dorsal Anterior Cingulate Cortex during a Mixed-Strategy Game

Seo, H., & Lee, D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2369-07.2007 · Cited by 324

The need to approximate the use-case in clinical machine learning

Saeb, S., Lonini, L., Jayaraman, A., Mohr, D. C., & Kording, K. P. (2017) · GigaScience · doi:10.1093/gigascience/gix019 · Cited by 367

Polymorphisms within the C-Reactive Protein (CRP) Promoter Region Are Associated with Plasma CRP Levels

Carlson, C. S., Aldred, S. F., Lee, P. K., Tracy, R. P., Schwartz, S. M., Rieder, M. J., Liu, K., Williams, O. D., Iribarren, C., Lewis, E. C., et al. (2005) · The American Journal of Human Genetics · doi:10.1086/431366 · Cited by 326

Structured Sparsity through Convex Optimization

Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2012) · Statistical Science · doi:10.1214/12-sts394 · Cited by 335

Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

Myslín, M., Zhu, S., Chapman, W. W., & Conway, M. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2534 · Cited by 336

Estimating brain functional connectivity with sparse multivariate autoregression

Valdes-Sosa, P. A., Sánchez‐Bornot, J. M., Lage‐Castellanos, A., Vega‐Hernández, M., Bosch‐Bayard, J., Melie‐García, L., & Canales-Rodríguez, E. J. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1654 · Cited by 323

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

Tsanas, A., Little, M. A., McSharry, P., & Ramig, L. O. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0456 · Cited by 323

Highly comparative time-series analysis: the empirical structure of time series and their methodsPreprint

Fulcher, B., Little, M. A., & Jones, N. S. (2013) · Cited by 325

Measuring the predictability of life outcomes with a scientific mass collaboration

Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915006117 · Cited by 322

Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds

Shortridge, J., Guikema, S. D., & Zaitchik, B. F. (2016) · Hydrology and earth system sciences · doi:10.5194/hess-20-2611-2016 · Cited by 340

A checklist for maximizing reproducibility of ecological niche models

Feng, X., Park, D. S., Walker, C., Peterson, A. T., Merow, C., & Papeş, M. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0972-5 · Cited by 323

The Bayesian elastic net

Li, Q., & Lin, N. (2010) · Bayesian Analysis · doi:10.1214/10-ba506 · Cited by 345

Machine Learning-based traffic prediction models for Intelligent Transportation Systems

Boukerche, A., & Wang, J. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107530 · Cited by 353

Model Averaging and Its Use in Economics

Steel, M. F. J. (2020) · Journal of Economic Literature · doi:10.1257/jel.20191385 · Cited by 321

Computer Mediated Transactions

Varian, H. R. (2010) · American Economic Review · doi:10.1257/aer.100.2.1 · Cited by 349

Predictors of enhanced response with benralizumab for patients with severe asthma: pooled analysis of the SIROCCO and CALIMA studies

FitzGerald, J. M., Bleecker, E. R., Menzies-Gow, A. N., Zangrilli, J., Hirsch, I., Metcalfe, P., Newbold, P., & Goldman, M. A. (2017) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(17)30344-2 · Cited by 321

A unified framework for stochastic optimization

Powell, W. B. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.07.014 · Cited by 350

Predicting survival from microarray data—a comparative study

Bøvelstad, H. M., Nygård, S., Størvold, H. L., Aldrin, M., Borgan, Ø., Frigessi, A., & Lingjærde, O. C. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm305 · Cited by 319

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

Lajnef, T., Chaibi, S., Ruby, P. M., Aguera, P., Eichenlaub, J., Samet, M., Kachouri, A., & Jerbi, K. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.01.022 · Cited by 319

Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

Engelmann, J., & Lessmann, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114582 · Cited by 358

Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

Senoner, J., Netland, T., & Feuerriegel, S. (2022) · Management Science · doi:10.1287/mnsc.2021.4190 · Cited by 317

Automated classification of bird and amphibian calls using machine learning: A comparison of methods

Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., & Aide, T. M. (2009) · Ecological Informatics · doi:10.1016/j.ecoinf.2009.06.005 · Cited by 317

A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems

Delegido, J., Verrelst, J., Meza, C. M., Rivera-Caicedo, J. P., Alonso, L., & Moreno, J. (2013) · European Journal of Agronomy · doi:10.1016/j.eja.2012.12.001 · Cited by 317

Anatomical connectivity patterns predict face selectivity in the fusiform gyrus

Saygin, Z. M., Osher, D. E., Koldewyn, K., Reynolds, G., Gabrieli, J. D. E., & Saxe, R. R. (2011) · Nature Neuroscience · doi:10.1038/nn.3001 · Cited by 326

A survey of temporal data mining

Laxman, S., & SASTRY, P. S. (2006) · Sadhana · doi:10.1007/bf02719780 · Cited by 327

Decision support from financial disclosures with deep neural networks and transfer learning

Kraus, M., & Feuerriegel, S. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.10.001 · Cited by 312

A survey of deep meta-learning

Huisman, M., van Rijn, J. N., & Plaat, A. (2021) · Data Archiving and Networked Services (DANS) · doi:10.1007/s10462-021-10004-4#rightslink · Cited by 316

A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

Yang, Z., & Michailidis, G. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv544 · Cited by 295

Large-scale sequestration of atmospheric carbon via plant roots in natural and agricultural ecosystems: why and how

Kell, D. B. (2012) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2011.0244 · Cited by 339

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Mainali, K. P., Warren, D. L., Dhileepan, K., McConnachie, A., Strathie, L., Hassan, G., Karki, D., Shrestha, B. B., & Parmesan, C. (2015) · Global Change Biology · doi:10.1111/gcb.13038 · Cited by 314

Automated support for classifying software failure reports

Podgurski, A., Leon, D., Francis, P., Masri, W., & Minch, M. (2003) · Cited by 314

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review

Fernandes, M. P., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S. N., & Sousa, J. M. C. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101762 · Cited by 346

Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration

Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P. (2020) · New England Journal of Medicine · doi:10.1056/nejmsa2001090 · Cited by 314

Spatial cross-validation is not the right way to evaluate map accuracy

Wadoux, A. M. J.-C., Heuvelink, G. B. M., de Bruin, S., & Brus, D. J. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109692 · Cited by 313

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening

Basith, S., Manavalan, B., Shin, T. H., & Lee, G. (2020) · Medicinal Research Reviews · doi:10.1002/med.21658 · Cited by 316

Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., & Mouazen, A. M. (2015) · Soil and Tillage Research · doi:10.1016/j.still.2015.07.021 · Cited by 312

Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time

Zhu, Z., Woodcock, C. E., Holden, C. E., & Yang, Z. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.02.009 · Cited by 312

A strategy for extracting and analyzing large-scale quantitative epistatic interaction data

Collins, S. R., Schuldiner, M., Krogan, N. J., & Weissman, J. S. (2006) · Genome biology · doi:10.1186/gb-2006-7-7-r63 · Cited by 307

Accurate inference of shoot biomass from high-throughput images of cereal plants

Golzarian, M. R., Frick, R. A., Rajendran, K., Berger, B., Roy, S. J., Tester, M. A., & Lun, D. S. (2011) · Plant Methods · doi:10.1186/1746-4811-7-2 · Cited by 328

Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

Gu, W., Xiang, C., Venkatesh, Y. V., Huang, D., & Lin, H. T. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.05.006 · Cited by 312

How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

Ding, C., Cao, X., & Liu, C. (2019) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2019.04.011 · Cited by 313

Matrix Completion and Low-Rank SVD via Fast Alternating Least SquaresPreprint

Hastie, T. J., Mazumder, R., Lee, J. D., & Zadeh, R. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.2596 · Cited by 311

Separate neural representations for physical pain and social rejection

Woo, C.-W., Koban, L., Kross, E., Lindquist, M. A., Banich, M. T., Ruzic, L., Andrews-Hanna, J. R., & Wager, T. D. (2014) · Nature Communications · doi:10.1038/ncomms6380 · Cited by 310

Non-linear variable selection for artificial neural networks using partial mutual information

May, R. J., Maier, H. R., Dandy, G. C., & Fernando, T. M. K. G. (2008) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2008.03.007 · Cited by 309

A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

Wei, L., Yang, Y., Nishikawa, R. M., & Jiang, Y. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.842457 · Cited by 311

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment

Zhan, Y., Luo, Y., Deng, X., Grieneisen, M. L., Zhang, M., & Di, B. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.10.029 · Cited by 311

ObliVM: A Programming Framework for Secure Computation

Liu, C., Wang, X. S., Nayak, K., Huang, Y., & Shi, E. (2015) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2015.29 · Cited by 321

Spatial distribution of soil organic carbon stocks in France

Martin, M. P., Wattenbach, M., Smith, P., Meersmans, J., Jolivet, C. C., Boulonne, L., & Arrouays, D. (2011) · Biogeosciences · doi:10.5194/bg-8-1053-2011 · Cited by 342

Partial least squares path modeling: Time for some serious second thoughts

Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016) · Journal of Operations Management · doi:10.1016/j.jom.2016.05.002 · Cited by 328

Big data analytics for knowledge generation in tourism destinations – A case from Sweden

Fuchs, M., Höpken, W., & Lexhagen, M. (2014) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2014.08.002 · Cited by 308

scds: computational annotation of doublets in single-cell RNA sequencing data

Bais, A. S., & Kostka, D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz698 · Cited by 308

A method for calling gains and losses in array CGH data

Wang, P., Kim, Y., Pollack, J. R., Narasimhan, B., & Tibshirani, R. J. (2004) · Biostatistics · doi:10.1093/biostatistics/kxh017 · Cited by 302

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer, C., & Gerlich, D. W. (2013) · Journal of Cell Science · doi:10.1242/jcs.123604 · Cited by 312

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., & Papritz, A. (2018) · SOIL · doi:10.5194/soil-4-1-2018 · Cited by 328

The reusable holdout: Preserving validity in adaptive data analysis

Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015) · Science · doi:10.1126/science.aaa9375 · Cited by 307

Internet-based surveillance systems for monitoring emerging infectious diseases

Milinovich, G. J., Williams, G. M., Clements, A., & Hu, W. (2013) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(13)70244-5 · Cited by 309

Breast cancer diagnosis using GA feature selection and Rotation Forest

Aličković, E., & Subaşı, A. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-2103-9 · Cited by 312

The Elements of Statistical Learning -- Data Mining, . . .

Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2013)

The Elements of Statistical Learning: Data Mining, Inference and Prediction. By

Beezer, R. A., Hastie, T., Tibshirani, R., & Springer, J. F. (2002)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by HASTIE, T., TIBSHIRANI, R., and FRIEDMAN, J.

Rosa, G. J. M. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01516.x · Cited by 31

An Introduction to Statistical Learning

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106

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